Modeling the HIV Cascade of Care Using Routinely Collected Clinical Data to Guide Programmatic Interventions and Policy Decisions.

BACKGROUND: The HIV care cascade is a framework to examine effectiveness of HIV programs and progress toward global targets to end the epidemic but has been conceptualized as a unidirectional process that ignores cyclical care patterns. We present a dynamic cascade that accounts for patient "churn" and apply novel analytic techniques to readily available clinical data to robustly estimate program outcomes and efficiently assess progress toward global targets. METHODS: Data were assessed for 35,649 people living with HIV and receiving care at 78 clinics in East Africa between 2014 and 2020. Patients were aged ≥15 years and had ≥1 viral load measurements. We used multi-state models to estimate the probability of being in 1 of 5 states of a dynamic HIV cascade: (1) in HIV care but not on antiretroviral therapy (ART), (2) on ART, (3) virally suppressed, (4) in a gap-in-care, and (5) deceased and compared these among subgroups. To assess progress toward global targets, we summed those probabilities across patients and generated population-level proportions of patients on ART and virally suppressed in mid-2020. RESULTS: One year after enrollment, 2.8% of patients had not initiated ART, 86.7% were receiving ART, 57.4% were virally suppressed, 10.2% were disengaged from care, and 0.3% had died. At 5 years, the proportion on ART remained steady but viral suppression increased to 77.2%. Of those aged 15-25, >20% had disengaged from care and <60% were virally suppressed. In mid-2020, 90.1% of the cohort was on ART, 90.7% of whom had suppressed virus. CONCLUSIONS: Novel analytic approaches can characterize patient movement through a dynamic HIV cascade and, importantly, by capitalizing on readily available data from clinical cohorts, offer an efficient approach to estimate population-level proportions of patients on ART and virally suppressed. Significant progress toward global targets was observed in our cohort but challenges remain among younger patients.

FIRST- LINE ANTIRETROVIRAL TREATMENT FAILURE IN EAST AFRICAN CHILDREN.

Objectives: To describe the incidence of antiretroviral treatment failure and associated factors in a pediatric clinical cohort within the East African International epidemiology Databases to Evaluate AIDS (EA-IeDEA) consortium. Design: A retrospective cohort study. Clinical treatment failure was defined as advancement in clinical WHO stage, or CDC class at least 24 weeks after initiation of treatment. Immunological failure was defined as developing or returning to the following age-related immunological thresholds after at least 24 weeks on treatment; CD4 count of <200 or CD4%<10% for children aged 2-5 years and CD4 count of < 100 for a child aged > 5years. Setting: The study utilized the electronic medical records of HIV-infected pediatric patients enrolled into the EA-IeDEA consortium clinics from January 2005 to August 2012. Results: A total of 5927 children were included in the analysis. The estimated cumulative incidence of clinical ART treatment failure at one year and four years post ART initiation was11.5% and 31% respectively, while that of immunological treatment failure was at 3% and 22.5% respectively. The main factors associated with clinical failure were advanced clinical stage at ART-initiation, year started ART and residing in a rural area. Factors associated with immunological failure were male gender and age of the child at ART-initiation. Only 6% of those identified as having clinical treatment failure were switched to second line treatment during the four years of follow-up. Conclusion: The probability of clinical and immunologic failure was relatively high and increased with time.

The Causal Effect of Tracing by Peer Health Workers on Return to Clinic Among Patients Who Were Lost to Follow-up From Antiretroviral Therapy in Eastern Africa: A "Natural Experiment" Arising From Surveillance of Lost Patients.

BACKGROUND.: The effect of tracing human immunodeficiency virus (HIV)-infected patients who are lost to follow-up (LTFU) on reengagement has not been rigorously assessed. We carried out an ex post analysis of a surveillance study in which LTFU patients were randomly selected for tracing to identify the effect of tracing on reengagement. METHODS.: We evaluated HIV-infected adults on antiretroviral therapy who were LTFU (>90 days late for last visit) at 14 clinics in Uganda, Kenya, and Tanzania. A random sample of LTFU patients was selected for tracing by peer health workers. We assessed the effect of selection for tracing using Kaplan-Meier estimates of reengagement among all patients as well as the subset of LTFU patients who were alive, contacted in person by the tracer, and out of care. RESULTS.: Of 5781 eligible patients, 991 (17%) were randomly selected for tracing. One year after selection for tracing, 13.3% (95% confidence interval [CI], 11.1%-15.3%) of those selected for tracing returned compared with 10.0% (95% CI, 9.1%-10.8%) of those not randomly selected, an adjusted risk difference of 3.0% (95% CI, .7%-5.3%). Among patients found to be alive, personally contacted, and out of care, tracing increased the absolute probability of return at 1 year by 22% (95% CI, 7.1%-36.2%). The effect of tracing on rate of return to clinic decayed with a half-life of 7.0 days after tracing (95% CI, 2.6 %-12.9%). CONCLUSIONS.: Tracing interventions increase reengagement, but developing methods for targeting LTFU patients most likely to benefit can make this practice more efficient.

Patient-reported factors associated with reengagement among HIV-infected patients disengaged from care in East Africa.

OBJECTIVE: Engagement in care is key to successful HIV treatment in resource-limited settings; yet little is known about the magnitude and determinants of reengagement among patients out of care. We assessed patient-reported reasons for not returning to clinic, identified latent variables underlying these reasons, and examined their influence on subsequent care reengagement. DESIGN: We used data from the East Africa International Epidemiologic Databases to Evaluate AIDS to identify a cohort of patients disengaged from care (>3 months late for last appointment, reporting no HIV care in preceding 3 months) (n = 430) who were interviewed about reasons why they stopped care. Among the 399 patients for whom follow-up data were available, 104 returned to clinic within a median observation time of 273 days (interquartile range: 165-325). METHODS: We conducted exploratory and confirmatory factor analyses (EFA, CFA) to identify latent variables underlying patient-reported reasons, then used these factors as predictors of time to clinic return in adjusted Cox regression models. RESULTS: EFA and CFA findings suggested a six-factor structure that lent coherence to the range of barriers and motivations underlying care disengagement, including poverty, transport costs, and interference with work responsibilities; health system 'failures,' including poor treatment by providers; fearing disclosure of HIV status; feeling healthy; and treatment fatigue/seeking spiritual alternatives to medicine. Factors related to poverty and poor treatment predicted higher rate of return to clinic, whereas the treatment fatigue factor was suggestive of a reduced rate of return. CONCLUSION: Certain barriers to reengagement appear easier to overcome than factors such as treatment fatigue. Further research will be needed to identify the easiest, least expensive interventions to reengage patients lost to HIV care systems. Interpersonal interventions may continue to play an important role in addressing psychological barriers to retention.

Retention in Care and Patient-Reported Reasons for Undocumented Transfer or Stopping Care Among HIV-Infected Patients on Antiretroviral Therapy in Eastern Africa: Application of a Sampling-Based Approach.

BACKGROUND: Improving the implementation of the global response to human immunodeficiency virus requires understanding retention after starting antiretroviral therapy (ART), but loss to follow-up undermines assessment of the magnitude of and reasons for stopping care. METHODS: We evaluated adults starting ART over 2.5 years in 14 clinics in Uganda, Tanzania, and Kenya. We traced a random sample of patients lost to follow-up and incorporated updated information in weighted competing risks estimates of retention. Reasons for nonreturn were surveyed. RESULTS: Among 18 081 patients, 3150 (18%) were lost to follow-up and 579 (18%) were traced. Of 497 (86%) with ascertained vital status, 340 (69%) were alive and, in 278 (82%) cases, updated care status was obtained. Among all patients initiating ART, weighted estimates incorporating tracing outcomes found that 2 years after ART, 69% were in care at their original clinic, 14% transferred (4% official and 10% unofficial), 6% were alive but out of care, 6% died in care (<60 days after last visit), and 6% died out of care (≥ 60 days after last visit). Among lost patients found in care elsewhere, structural barriers (eg, transportation) were most prevalent (65%), followed by clinic-based (eg, waiting times) (33%) and psychosocial (eg, stigma) (27%). Among patients not in care elsewhere, psychosocial barriers were most prevalent (76%), followed by structural (51%) and clinic based (15%). CONCLUSIONS: Accounting for outcomes among those lost to follow-up yields a more informative assessment of retention. Structural barriers contribute most to silent transfers, whereas psychological and social barriers tend to result in longer-term care discontinuation.

Estimation of mortality among HIV-infected people on antiretroviral treatment in East Africa: a sampling based approach in an observational, multisite, cohort study.

BACKGROUND: Mortality in HIV-infected people after initiation of antiretroviral treatment (ART) in resource-limited settings is an important measure of the effectiveness and comparative effectiveness of the global public health response. Substantial loss to follow-up precludes accurate accounting of deaths and limits our understanding of effectiveness. We aimed to provide a better understanding of mortality at scale and, by extension, the effectiveness and comparative effectiveness of public health ART treatment in east Africa. METHODS: In 14 clinics in five settings in Kenya, Uganda, and Tanzania, we intensively traced a sample of patients randomly selected using a random number generator, who were infected with HIV and on ART and who were lost to follow-up (>90 days late for last scheduled visit). We incorporated the vital status outcomes for these patients into analyses of the entire clinic population through probability-weighted survival analyses. FINDINGS: We followed 34 277 adults on ART from Mbarara and Kampala in Uganda, Eldoret, and Kisumu in Kenya, and Morogoro in Tanzania. The median age was 35 years (IQR 30-42), 11 628 (34%) were men, and median CD4 count count before therapy was 154 cells per µL (IQR 70-234). 5780 patients (17%) were lost to follow-up, 991 (17%) were selected for tracing between June 10, 2011, and Aug 27, 2012, and vital status was ascertained for 860 (87%). With incorporation of outcomes from the patients lost to follow-up, estimated 3 year mortality increased from 3·9% (95% CI 3·6-4·2) to 12·5% (11·8-13·3). The sample-corrected, unadjusted 3 year mortality across settings was lowest in Mbarara (7·2%) and highest in Morogoro (23·6%). After adjustment for age, sex, CD4 count before therapy, and WHO stage, the sample-corrected hazard ratio comparing the settings with highest and lowest mortalities was 2·2 (95% CI 1·5-3·4) and the risk difference for death at 3 years was 11% (95% CI 5·0-17·7). INTERPRETATION: A sampling-based approach is widely feasible and important to an understanding of mortality after initiation of ART. After adjustment for measured biological drivers, mortality differs substantially across settings despite delivery of a similar clinical package of treatment. Implementation research to understand the systems, community, and patients' behaviours driving these differences is urgently needed. FUNDING: The US National Institutes of Health and President's Emergency Fund for AIDS Relief.

Age-specific and sex-specific weight gain norms to monitor antiretroviral therapy in children in low-income and middle-income countries.

BACKGROUND: Viral load and CD4% are often not available in resource-limited settings for monitoring children's responses to antiretroviral therapy (ART). We aimed to construct normative curves for weight gain at 6, 12, 18, and 24 months following initiation of ART in children, and to assess the association between poor weight gain and subsequent responses to ART. DESIGN: Analysis of data from HIV-infected children younger than 10 years old from African and Asian clinics participating in the International epidemiologic Databases to Evaluate AIDS. METHODS: The generalized additive model for location, scale, and shape was used to construct normative percentile curves for weight gain at 6, 12, 18, and 24 months following ART initiation. Cox proportional models were used to assess the association between lower percentiles (< 50th) of weight gain distribution at the different time points and subsequent death, virological suppression, and virological failure. RESULTS: Among 7173 children from five regions of the world, 45% were underweight at baseline. Weight gain below the 50th percentile at 6, 12, 18, and 24 months of ART was associated with increased risk of death, independent of baseline characteristics. Poor weight gain was not associated with increased hazards of virological suppression or virological failure. CONCLUSION: Monitoring weight gain on ART using age-specific and sex-specific normative curves specifically developed for HIV-infected children on ART is a simple, rapid, sustainable tool that can aid in the identification of children who are at increased risk of death in the first year of ART.

Immunodeficiency at the start of combination antiretroviral therapy in low-, middle-, and high-income countries.

OBJECTIVE: To describe the CD4 cell count at the start of combination antiretroviral therapy (cART) in low-income (LIC), lower middle-income (LMIC), upper middle-income (UMIC), and high-income (HIC) countries. METHODS: Patients aged 16 years or older starting cART in a clinic participating in a multicohort collaboration spanning 6 continents (International epidemiological Databases to Evaluate AIDS and ART Cohort Collaboration) were eligible. Multilevel linear regression models were adjusted for age, gender, and calendar year; missing CD4 counts were imputed. RESULTS: In total, 379,865 patients from 9 LIC, 4 LMIC, 4 UMIC, and 6 HIC were included. In LIC, the median CD4 cell count at cART initiation increased by 83% from 80 to 145 cells/µL between 2002 and 2009. Corresponding increases in LMIC, UMIC, and HIC were from 87 to 155 cells/µL (76% increase), 88 to 135 cells/µL (53%), and 209 to 274 cells/µL (31%). In 2009, compared with LIC, median counts were 13 cells/µL [95% confidence interval (CI): -56 to +30] lower in LMIC, 22 cells/µL (-62 to +18) lower in UMIC, and 112 cells/µL (+75 to +149) higher in HIC. They were 23 cells/µL (95% CI: +18 to +28 cells/µL) higher in women than men. Median counts were 88 cells/µL (95% CI: +35 to +141 cells/µL) higher in countries with an estimated national cART coverage >80%, compared with countries with <40% coverage. CONCLUSIONS: Median CD4 cell counts at the start of cART increased 2000-2009 but remained below 200 cells/µL in LIC and MIC and below 300 cells/µL in HIC. Earlier start of cART will require substantial efforts and resources globally.